Evidence map›Paper›PMID 34112939›Full record

ArticleNPJ digital medicine2021

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.

Ania Syrowatka, Masha Kuznetsova, Ava Alsubai, Adam L Beckman, Paul A Bain, Kelly Jean Thomas Craig, Jianying Hu, Gretchen Purcell Jackson, Kyu Rhee, David W Bates

Abstract readScoping Review
In one paragraph

Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

54 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Early Warning of Infectious Disease Outbreaks Using Social Media and Digital Data: A Scoping Review.International journal of environmental research and public health · 2025
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  11. The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses.International journal of environmental research and public health · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ania SyrowatkaDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA. asyrowatka@bwh.harvard.edu.ORCID http://orcid.org/0000-0002-7161-9770
Masha KuznetsovaHarvard Business School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-6710-526X
Ava AlsubaiDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Adam L BeckmanHarvard Medical School, Boston, MA, USA.
Paul A BainCountway Library of Medicine, Harvard Medical School, Boston, MA, USA.
Kelly Jean Thomas CraigIBM Watson Health, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-9954-2795
Jianying HuIBM Research, Center for Computational Health, Yorktown Heights, NY, USA.
Gretchen Purcell JacksonIBM Watson Health, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-3242-8058
Kyu RheeIBM Watson Health, Cambridge, MA, USA.
David W BatesDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0001-6268-1540

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Identifiers

PMID34112939
PMCPMC8192906

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.